AU2021103663A4 - Method, system and device for measuring building operational energy consumption carbon emissions with high resolution - Google Patents
Method, system and device for measuring building operational energy consumption carbon emissions with high resolution Download PDFInfo
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Abstract
OF THE DISCLOSURE
A method, system and device for measuring building operational energy consumption carbon
emissions with high resolution are disclosed. The method includes: collecting auxiliary variable
information of each provincial-level administrative region based on building characteristics, social
and economic conditions, regional climate background, and ambient microclimate of buildings,
and creating respective grid maps of the auxiliary variable information; conducting zonal statistics
on the grid maps by using boundaries of the provincial-level administrative regions and sub
provincial-level administrative regions as boundary ranges, to obtain statistics of each of the grid
maps; constructing, based on a partial least squares regression method, a provincial downscaling
model by using the statistics as independent variables and the building operational energy
consumption carbon emissions of the provincial-level administrative regions as a dependent
variable; and inputting, based on scale invariance of a relational model, grid map statistics of the
sub-provincial-level administrative regions into the provincial downscaling model by using a
stepwise downscaling method, and modifying the inputted statistics, to obtain building operational
energy consumption carbon emissions of the sub-provincial-level administrative regions. The
present invention can implement fine quantitative measurement for building operational energy
consumption carbon emissions, and archives high accuracy.
ABSTRACT DRAWING
101
Calculate building operational energy consumption carbon emissions of each
provincial-level administrative region
102
Collect auxiliary variable information of each provincial-level administrative
region based on building characteristics, social and economic conditions, regional
climate background, and ambient microclimate of buildings, and create respective
grid maps of the auxiliary variable information, where the auxiliary variable
information includes a building area, a total population, a gross regional product,
a temperature, heating degree days, and cooling degree days
103
Conduct zonal statistics on all the grid maps by using boundaries of
administrative regions as boundary ranges, to obtain statistics of each of the grid
maps, where the statistics include a gross regional product per unit area, a total
population per unit area, an average temperature, average heating degree days,
and average cooling degree days within each administrative boundary, and the
administrative regions include the provincial-level administrative regions and
sub-provincial-level administrative regions
104
Construct, based on a partial least squares regression method, a provincial
downscaling model by using the statistics as independent variables and the
operational energy consumption and carbon emissions of buildings of the
provincial-level administrative regions as a dependent variable
105
Input, based on scale invariance of a relational model, grid map statistics of the
sub-provincial-level administrative regions into the provincial downscaling
model by using a stepwise downscaling method, and modify the inputted
statistics by using a consistency-in-aggregation approach, to obtain building
operational energy consumption carbon emissions of the sub-provincial-level
administrative regions
FIG. 1
Description
101
Calculate building operational energy consumption carbon emissions of each provincial-level administrative region 102
Collect auxiliary variable information of each provincial-level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and create respective grid maps of the auxiliary variable information, where the auxiliary variable information includes a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days
103
Conduct zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, where the statistics include a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions include the provincial-level administrative regions and sub-provincial-level administrative regions 104
Construct, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the operational energy consumption and carbon emissions of buildings of the provincial-level administrative regions as a dependent variable 105
Input, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modify the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions
FIG. 1
[01] The present disclosure relates to the field of building energy efficiency and statistics, and in particular, to a method, system and device for measuring building operational energy consumption carbon emissions with high resolution.
[02] In the context of rapid global urbanization, urban energy consumption is rising year by year. Energy shortage and energy pollutions are getting worse. The continuous growth of building operational energy consumption carbon emissions restricts the sustainable development of cities. As the foundation of building energy conservation, refined statistics of building energy consumption carbon emissions are attracting more attentions. Researches show that a microclimate environment with local impact on buildings is formed within a 1km buffer zone of the buildings. However, in the past, scholars mostly focused on building carbon emissions in the regional macroclimate or mesoclimate background. Researches on building carbon emissions within the impact range of microclimate of urban buildings are rare.
[03] Therefore, there is an urgent need for a method that can finely quantify building operational energy consumption carbon emissions with high resolution within the impact range of urban microclimate.
[04] Based on above problems, it is necessary to provide a method, system and device for measuring building operational energy consumption carbon emissions with high resolution, to finely quantify building operational energy consumption carbon emissions with high resolution within the impact range of urban microclimate, thereby improving the accuracy of measurement.
[05] To implement the foregoing objectives, the present disclosure provides the following solutions.
[06] A method for measuring building operational energy consumption carbon emissions with high resolution, including: calculating building operational energy consumption carbon emissions of each provincial-level administrative region; collecting auxiliary variable information of each provincial-level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and creating respective grid maps of the auxiliary variable information, where the auxiliary variable information includes a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days; conducting zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, where the statistics include a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions include the provincial-level administrative regions and sub-provincial level administrative regions; constructing, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable; and inputting, based on scale invariance of a relational model, grid map statistics of the sub-provincial level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modifying the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub provincial-level administrative regions.
[07] Optionally, after the inputting, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modifying the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, the method further includes: performing a spatial distribution simulation based on the building operational energy consumption carbon emissions of the provincial-level administrative regions and the building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, to obtain a spatial distribution simulation result of building operational energy consumption carbon emissions corresponding to each grid map of the administrative regions.
[08] Optionally, the calculating building operational energy consumption carbon emissions of each provincial-level administrative region includes: obtaining energy consumption data of each provincial-level administrative region within the same or close building climate zones, where the energy consumption data includes primary energy consumption, heat consumption, and electricity consumption; and calculating the building operational energy consumption carbon emissions of each provincial-level administrative region according to the energy consumption data, where the building operational energy consumption carbon emissions are C02 emissions in an energy consumption process of buildings in an operational stage.
[09] Optionally, a calculation formula for the building operational energy consumption carbon emissions of the provincial-level administrative region is as follows:
BECCEi, = ECip x aip + ECihx aih + ECie x ae; where BECCEip is building operational energy consumption carbon emissions of provincep in year i, ECip is primary energy consumption of province p in year i, ECih is heat consumption of province p in year i, ECe is electricity consumption of province p in year i, air is a carbon emission factor corresponding to the primary energy consumption of province p in year i, aihis acarbon emission factor corresponding to the heat consumption of province p in year i, and aie a carbon emission factor corresponding to the electricity consumption of province p in year i.
[10] Optionally, the sub-provincial-level administrative region includes one or more of a prefecture-level administrative region, a county-level administrative region, and a township-level administrative region.
[11] The present disclosure further provides a system for measuring building operational energy consumption carbon emissions with high resolution, including: a carbon emissions calculation module, configured to calculate building operational energy consumption carbon emissions of each provincial-level administrative region; a grid map creating module, configured to collect auxiliary variable information of each provincial level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and create respective grid maps of the auxiliary variable information, where the auxiliary variable information includes a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days; a zonal statistics module, configured to conduct zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, where the statistics include a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions include the provincial-level administrative regions and sub-provincial-level administrative regions; a provincial downscaling model construction module, configured to construct, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable; and a carbon emissions stepwise simulation module, configured to input, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modify the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions.
[12] Optionally, the system for measuring building operational energy consumption carbon emissions with high resolution further includes: a spatial simulation module, configured to perform a spatial distribution simulation based on the building operational energy consumption carbon emissions of the provincial-level administrative regions and the building operational energy consumption carbon emissions of the sub-provincial level administrative regions, to obtain a spatial distribution simulation result of building operational energy consumption carbon emissions corresponding to each grid map of the administrative regions.
[13] Optionally, the carbon emissions calculation module specifically includes: an energy consumption data obtaining unit, configured to obtain energy consumption data of each provincial-level administrative region within the same or close building climate zones, where the energy consumption data includes primary energy consumption, heat consumption, and electricity consumption; and a carbon emissions calculation unit, configured to calculate the building operational energy consumption carbon emissions of each provincial-level administrative region according to the energy consumption data, where the building operational energy consumption carbon emissions areC02 emissions in an energy consumption process of buildings in an operational stage.
[14] The present disclosure further provides a terminal device, including a processor and a memory connected to the processor, where the memory is configured to store a computer program that includes program instructions, and the processor is configured to invoke the program instructions to perform the foregoing method measuring building operational energy consumption carbon emissions with high resolution.
[15] The present disclosure further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program that includes program instructions, and when executed by a processor, the program instructions cause the processor to perform the foregoing method for measuring building operational energy consumption carbon emissions with high resolution.
[16] Compared with the prior art, the present disclosure has the following beneficial effects.
[17] The present invention provides a method, system and device for measuring building operational energy consumption carbon emissions with high resolution. Auxiliary variable information is selected from four aspects that affect building energy consumption and carbon emissions, including building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings. Based on a partial least squares regression method, a multiple regression provincial downscaling model about building carbon emissions and auxiliary variables is constructed at the provincial scale. Based on scale invariance of the relational model, building operational energy consumption carbon emissions of sub-provincial-level administrative regions are obtained. The present disclosure realizes scientific quantitative measurement for information at a fine scale, and improves the accuracy of the measurement, thereby establishing a high-resolution grid database of building carbon emissions.
[18] In order to explain the technical solutions in embodiments of the present disclosure or in the prior art more clearly, the accompanying drawings required in the embodiments will be described below briefly. Apparently, the accompanying drawings in the following description show merely some embodiments of the present disclosure, and other drawings can be derived from these accompanying drawings by those of ordinary skill in the art without creative efforts.
[19] FIG. 1 is a flowchart of a method for measuring building operational energy consumption carbon emissions with high resolution according to an embodiment of the present disclosure;
[20] FIG. 2 is a schematic flowchart of a method for measuring building operational energy consumption carbon emissions with high resolution according to a more specific embodiment of the present disclosure;
[21] FIG. 3 is a schematic diagram of a spatial simulation grid of density of building operational energy consumption carbon emissions with high resolution in a range to be measured; and
[22] FIG. 4 is a structural diagram of a system for measuring building operational energy consumption carbon emissions with high resolution according to an embodiment of the present disclosure.
[23] The technical solutions of the embodiments of the present disclosure are clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are merely a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.
[24] To make the above objectives, features and advantages of the present disclosure clearer and more comprehensible, the present disclosure is described in further detail below with reference to the accompanying drawings and specific implementations.
[25] FIG. 1 is a flowchart of a method for measuring building operational energy consumption carbon emissions with high resolution according to an embodiment of the present disclosure. Referring to FIG. 1, the method for measuring building operational energy consumption carbon emissions with high resolution according to this embodiment includes the following steps: Step 101: Calculate building operational energy consumption carbon emissions of each provincial level administrative region.
[26] This step specifically includes: obtaining energy consumption data of provincial-level administrative regions in the same or close building climate zones, where the energy consumption data includes primary energy consumption, heat consumption, and electricity consumption; calculating the building operational energy consumption carbon emissions of each provincial-level administrative region according to the energy consumption data, where the building operational energy consumption carbon emissions are C02 emissions in an energy consumption process of buildings in an operational stage. The energy consumption data of the provincial-level administrative regions may be collected from an energy statistical yearbook.
[27] A calculation formula for the building operational energy consumption carbon emissions of the provincial-level administrative region is as follows: BECCEi, = ECip x aip + ECihx aih + ECie x ae; where BECCEip is building operational energy consumption carbon emissions of provincep in year i, ECip is primary energy consumption of province p in year i, ECih is heat consumption of province p in year i, ECie is electricity consumption of province p in year i, air is a carbon emission factor corresponding to the primary energy consumption of province p in year i, aih is a carbon emission factor corresponding to the heat consumption of province p in year i, and aie a carbon emission factor corresponding to the electricity consumption of province p in year i.
[28] Step 102: Collect auxiliary variable information of each provincial-level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and create respective grid maps of the auxiliary variable information, where the auxiliary variable information include a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days. Meteorological data (temperature, heating degree days, and cooling degree days) in the auxiliary variable information is collected from National Meteorological Information Center. The size of the grid map is an impact range of urban building microclimate, which may be customized as required.
[29] Step 103: Conduct zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, where the statistics include a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions include the provincial-level administrative regions and sub-provincial-level administrative regions. The sub-provincial-level administrative region includes one or more of a prefecture-level administrative region, a county-level administrative region, and a township-level administrative region.
[30] Step 104: Construct, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable.
[31] Step 105: Input, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modify the inputted statistics by using a consistency-in aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions.
[32] As an alternative implementation, after step 105, the method further includes: performing a spatial distribution simulation based on the building operational energy consumption carbon emissions of the provincial-level administrative regions and the building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, to obtain a spatial distribution simulation result of building operational energy consumption carbon emissions corresponding to each grid map of the administrative regions.
[33] The following provides a more specific embodiment. FIG. 2 is a schematic flowchart of a method for measuring building operational energy consumption carbon emissions with high resolution according to a more specific embodiment of the present disclosure. Referring to FIG. 2, the method includes the following steps: SI: Collect various types of energy consumption data of multiple provincial-level administrative regions in the same or close building climate zones, and calculate building operational energy consumption carbon emissions of each provincial-level administrative region.
[34] In this embodiment, the method for measuring building operational energy consumption carbon emissions with high resolution is described in detail by using a downscaling instance of Xiamen City as an example. Data in year 2015 is collected.
[35] In this embodiment, Fujian Province, Guangdong Province, Guangxi Province, Yunnan Province and Hainan Province, which are located in hot summer and warm winter zone and in warm zone are selected, and the primary energy consumption, heat consumption and electricity energy consumption of coal, oil, natural gas and the like are obtained from the energy balance sheet of each province in China Energy Statistical Yearbook. The energy balance sheet is shown as follows:
[36] Table 1 Item Raw Clean Other Briq Coke Coke Gasol Diesel Liquefi Natu Heat Electric coal ed washe uette oven me oil ed ral energy ity coal d coal s gas petrole gas um gas 4. End-use 1105.5 0.31 0.02 12.2 220.4 7.66 371.5 237.0 40.52 44.4 16557.63 781.22 consumption 9 0 5 3 0 3 5. Wholesale, 41.56 0.87 21.71 8.19 13.57 4.48 1339.20 76.47 retailing, and hotel and catering industry 6. Others 208.37 5.78 0.01 46.73 15.17 2.24 17.9 6178.11 218.46 4 7. Living 278.79 230.0 0.64 21.51 10.1 2851.00 139.33 consumption 0 5 Urban 72.49 223.6 14.76 9.89 2851.00 95.38 2 Rural 206.30 6.38 0.64 6.75 0.26 43.95
[37] In Table 1, raw coal, cleaned coal, other washed coal, briquettes, coke, gasoline, diesel oil, and liquefied petroleum gas are all in the unit of 10 4 tn; coke oven gas and natural gas are in the unit of 10 8 cu.m; heat energy is in the unit of1 1 0 kJ, and electricity is in the unit of10 8 kW-h.
[38] S2: Select various types of data from four aspects that affect building carbon emissions, including building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, to serve as auxiliary variables, collect auxiliary variable information of the provincial-level administrative regions, and establish respective grid maps of the auxiliary variable information.
[39] The size of the grid is an impact range of urban building microclimate, which may be customized as required. In this embodiment, the grid resolution is set to 1km.
[40] In this embodiment, four factors that affect building carbon emissions: gross regional product GDP, total population POP, temperature TEM, and cooling degree days CDD, are selected. Gross regional product per unit area GDP', total population per unit area POP', regional annual average temperature TEM', and regional annual average cooling degree days CDD' are used as four pieces of auxiliary variable information for establishing the downscaling model.
[41] The 1km-resolution grid maps of GDP and POP are obtained from Institute of Geographic Sciences and Natural Resources Research, CAS. Based on the dataset of daily ground climate values from China meteorological stations published by National Meteorological Information Center, the 1km resolution grid maps of annual average temperature TEM and annual average cooling degree days CDD of China in 2015 are established through spatial interpolation using ANUSPLIN software which is commonly used internationally.
[42] S3: By using boundaries of the provincial-level administrative regions and boundaries of subordinate administrative regions (sub-provincial-level administrative regions) as boundary ranges, collect statistics on a total value and an average value within each boundary, an area of each boundary range, and other information in the respective grid maps of the auxiliary variable information.
[43] Based on the boundary ranges of Fujian Province, Guangdong Province, Guangxi Province, Yunnan Province, Hainan Province (which are provincial-level administrative regions), nine prefecture-level cities of Fujian Province: Xiamen City, Fuzhou City, Longyan City, Nanping City, Ningde City, Putian City, Quanzhou City, Sanming City, and Zhangzhou City (which are prefecture-level administrative regions), districts under administration of Xiamen City (county level administrative regions), and street sub-districts and towns of Xiamen City (township-level administrative regions), statistics on the total GDP and total POP within each boundary are collected, and the corresponding gross regional product per unit area GDP' and population per unit area POP' are calculated according to the area of each boundary range; statistics about the TEM average value TEM' and the CDD average value CDD' within each boundary are collected.
[44] S4: Construct, based on partial least squares regression (PLSR), a provincial downscaling model by using the statistics of the provincial variables as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable.
[45] Statistics of gross regional product per unit area GDP', provincial population per unit area POP', provincial annual average temperature TEM', and provincial annual average cooling degree days CDD' are used as independent variables, and provincial building operational energy consumption carbon emissions per unit area BECCE' are used as a dependent variable. Data of each group of variables is shown in Table 2 below.
[46] Table 2 Province Independentvariables Dependentvariable GDP' POP' TEM' CDD' BECCE' (10,000 (people/km 2) ( 0C) (°C-d) Tons of C0 2/km 2 yuan/km 2 )
Fujian 2259.36 333.79 18.45 135.85 344.02 Guangdong 3700.94 614.52 21.75 314.59 635.56 Guangxi 703.85 201.73 20.52 251.64 103.88 Hainan 1005.98 275.46 24.93 495.23 205.93 Yunnan 352.27 123.08 15.44 48.17 78.91
[47] Partial least squares regression (PLSR) can overcome the shortcoming of multiple regression, typical correlation analysis and principal component analysis that a regression model cannot be established when the independent variables have multiple correlations, and is suitable for regression modeling when the sample size is smaller than the number of variables. In this embodiment, logarithmic processing is performed on each group of variables. A multiple regression downscaling model about building carbon emissions and auxiliary variables is constructed at the provincial scale by using partial least squares regression, and the established regression relationship is as follows: logo BECCE' 0.527 x logo GDP' + 0.796 x logo POP' + 5.691 x logo TEM' - 1.272 x logo CDD' - 5.733 (the coefficient of determination is 0.994)
[48] S5: Substitute statistics of the auxiliary variables of the sub-provincial-level administrative regions into the provincial downscaling model based on scale invariance of a relational model, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, correct simulated building carbon emissions of the sub-provincial-level administrative regions by using a consistency-in-aggregation approach, perform downscaling and correction stepwise based on the foregoing method, and finally substitute into grid data of the auxiliary variables, to obtain a spatial distribution simulation of building carbon emissions corresponding to each grid cell in the administrative regions.
[49] The downscaling is a process of converting data or information from low resolution to high resolution. One of the commonly used downscaling methods is to establish a behavior-driven equation of the independent variables with respect to the dependent variable at a large scale. Assuming that the statistical relationship of the variables remains consistent or can be expressed quantitatively in regions at different scales, based on the scale invariance of the relational model, such a statistical relationship is also true between factors at a smaller scale, thus achieving the objective of downscaling.
[50] Statistics of the gross regional product per unit area GDP', population per unit area POP', city annual average temperature TEM', and city annual average cooling degree days CDD' of nine prefecture-level cities in Fujian Province: Xiamen, Fuzhou, Longyan, Nanping, Ningde, Putian, Quanzhou, Sanming and Zhangzhou, are substituted into the foregoing provincial downscaling equation as independent variables. Building carbon emission values of the prefecture-level cities are obtained based on the scale invariance of the relational model. The sum of the simulated building carbon emissions of the nine cities is compared with the building carbon emissions of Fujian Province calculated according to the provincial energy balance sheet, and the simulated building carbon emissions per unit area of each city are corrected according to the consistency-in aggregation approach, thus obtaining information of building carbon emissions at the municipal scale. Then, building carbon emission data at fine scales of district level, street level, and town level in Xiamen are obtained by stepwise downscaling and correction using the foregoing method. Simulation results at each level are shown in Table 3 below:
[51] Table 3 Administrative Building carbon Administrative Building carbon Administrative Building carbon regions at different emissions regions at different emissions regions at different emissions levels Simulated levels Simulated levels Simulated amount amount amount Tonsof Tonsof Tonsof 2 C02/km 2 C02/km 2 C02/km Xiamen City 6938.70 Heshan Street Sub- 67572.43 Yundant Street Sub- 78610.55 district, Xiamen district, Xiamen Fuzhou City 420.82 Huli Street Sub- 65394.02 Zhonghua Street 70503.01 district, Xiamen Sub-district, Xiamen Longyan City 67.19 Jiangtou Street Sub- 76905.17 Datong Street Sub- 1639.02 district, Xiamen district, Xiamen Nanping City 31.62 Jinshan Street Sub- 58282.01 Fengnan Farm, 785.43 district, Xiamen Xiamen Ningde City 64.21 Bantou Reservoir, 2264.62 Hongtang Town, 1601.89 Xiamen Xiamen Putian City 961.13 Guankou Town, 3081.18 Lianhua Town, 409.24 Xiamen Xiamen Quanzhou City 1243.86 Houxi Town, 4930.58 Tingxi Town, 425.01 Xiamen Xiamen Sanming City 64.78 Jimei Street Sub- 7572.65 Baishalun Farm, 834.28 district, Xiamen Tongan District, Xiamen Zhangzhou City 322.74 Second Farm, Jimei 3105.93 Tingxi Protection 563.14 District, Xiamen Forest, Tongan District, Xiamen Haicang District, 3390.07 Bantou, Houxi 1375.40 Zhuba Overseas 589.54 Xiamen Town, Jimei Chinese Farm, District, Xiamen Tongan District, Xiamen Huli District, 64657.83 Tianma Overseas 8344.31 Wuxian Town, 792.61 Xiamen Chinese Farm, Jimei Xiamen District, Xiamen Jimei District, 4191.98 Tianma Breading 3695.12 Xike Town, Xiamen 2265.87 Xiamen Pig Farm, Jimei District, Xiamen Ximing District, 49601.70 Qiaoying Street 7961.73 Xiangping Street 1660.67 Xiamen Sub-district, Sub-district, Xiamen Xiamen Tongan District, 804.66 Sanxiu and Ningbao 7404.52 Xinmin Town, 1057.85 Xiamen Sub-district, Xiamen Xiamen Xiangan District, 1701.90 Xingbin Street Sub- 10911.88 Dadeng 1319.93 Xiamen district, Xiamen Town, Xiamen Dongfu Town, 3135.46 Xinglin Town, 6692.13 Maxiang 2724.24 Xiamen Xiamen Town, Xiamen First Farm, Dongfu 3604.49 Binhai Street Sub- 30075.03 Neicuo Town, 1335.92 Town, Xiamen district, Xiamen Xiamen Tianzhushan Forest 1105.61 Gulangyu Street 49684.70 Xindian 1864.62 Farm, Dongfu Sub-district, Town, Xiamen Town, Xiamen Xiamen Haicang Farm, 3095.90 Jialian Street Sub- 80512.92 Xinwei Town, 1429.23 Xiamen district, Xiamen Xiamen Maluan Bay, 4659.20 Kaiyuan Street Sub- 47554.46 Damaoshan 692.61
Haicang District, district, Xiamen Farm, Xinwei Xiamen Town, Xiamen Haicang Town, 4355.62 Lianqian Street 44300.04 Xiamen Sub-district, Xiamen Haifa and Haida 3384.67 Lujiang Street Sub- 81191.03 Sub-district, district, Xiamen Xiamen Xinyang Street Sub- 3684.75 Xiagang Street Sub- 76792.78 district, Xiamen district, Xiamen Dianqian Street 64053.57 Wucun Street Sub- 43478.19 Sub-district, district, Xiamen Xiamen
[52] Through substitution into 1km-resolution grid data of each auxiliary variable, a spatial simulation result of density of building carbon emissions of Xiamen City at 1km resolution is obtained, as shown in FIG. 3.
[53] In the embodiment of the present disclosure, auxiliary variable information is selected from four aspects that affect building energy consumption and carbon emissions, including social and economic conditions, building characteristics, regional climate background, and ambient microclimate of buildings. Based on a partial least squares regression method, a multiple regression provincial downscaling model about building carbon emissions and auxiliary variables is constructed at the provincial scale. Based on scale invariance of the relational model, information of building carbon emissions at fine scales of municipal level and street and town level is obtained. A high-resolution grid database of building carbon emissions is established, realizing scientific quantitative measurement for building operational energy consumption carbon emissions at fine resolution within the impact range of urban microclimate, and providing a method foundation and data support for researches on factors influencing the building carbon emissions within the impact range of urban microclimate.
[54] The present disclosure further provides a system for measuring building operational energy consumption carbon emissions with high resolution. FIG. 4 is a structural diagram of a system for measuring building operational energy consumption carbon emissions with high resolution according to an embodiment of the present disclosure. Referring to FIG. 4, the system of this embodiment includes: a carbon emissions calculation module 201, configured to calculate building operational energy consumption carbon emissions of each provincial-level administrative region; a grid map creating module 202, configured to collect auxiliary variable information of each provincial-level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and create respective grid maps of the auxiliary variable information, where the auxiliary variable information includes a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days; a zonal statistics module 203, configured to conduct zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, where the statistics include a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions include the provincial-level administrative regions and sub-provincial-level administrative regions; a provincial downscaling model construction module 204, configured to construct, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable; and a carbon emissions stepwise simulation module 205, configured to input, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modify the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions.
[55] As an optional implementation, the system for measuring building operational energy consumption carbon emissions with high resolution further includes: a spatial simulation module, configured to perform a spatial distribution simulation based on the building operational energy consumption carbon emissions of the provincial-level administrative regions and the building operational energy consumption carbon emissions of the sub-provincial level administrative regions, to obtain a spatial distribution simulation result of building operational energy consumption carbon emissions corresponding to each grid map of the administrative regions.
[56] As an optional implementation, the carbon emissions calculation module 201 specifically includes: an energy consumption data obtaining unit, configured to obtain energy consumption data of each provincial-level administrative region within a building climate zone, where the energy consumption data includes primary energy consumption, heat consumption, and electricity consumption; and a carbon emissions calculation unit, configured to calculate the building operational energy consumption carbon emissions of each provincial-level administrative region according to the energy consumption data, where the building operational energy consumption carbon emissions are C02 emissions in an energy consumption process of buildings in an operational stage.
[57] The present disclosure further provides a terminal device, including a processor and a memory connected to the processor. The memory is configured to store a computer program that includes program instructions, and the processor is configured to invoke the program instructions to perform the foregoing method measuring building operational energy consumption carbon emissions with high resolution.
[58] As an optional implementation, the terminal device may be a computing device such as a personal computer (PC), a palmtop computer, or a cloud server. The terminal device may include, but not limited to, a processor and a memory. Those skilled in the art may understand that the figure merely shows an example of the terminal device for measuring building operational energy consumption carbon emissions with high resolution, and does not limit the terminal device for measuring building operational energy consumption carbon emissions with high resolution. The terminal device may include more or fewer components, or some components may be combined, or different components may be used. For example, the terminal device may further include an input/output device, a network access device, a bus, or the like, which is not limited in the embodiments of the present disclosure.
[59] As an optional implementation, the processor may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or another programmable logic device, a discrete gate, a transistor logic device, a discrete hardware component, etc. The general purpose processor may be a microprocessor, or any conventional processor. The processor is a control center of the terminal device, and various parts of the whole terminal device are connected by using various interfaces and lines.
[60] As an optional implementation, the memory may be configured to store the computer program or modules. The processor implements various functions of the terminal device by running or executing the computer program and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system, and an application program required by at least one function. The data storage area may store data created by a mobile phone, and the like. In addition, the memory may include a high-speed random access memory, and may further include a non-volatile memory, such as a hard disk, an internal storage, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or another volatile solid-state storage device.
[61] The present disclosure further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program that includes program instructions, and when executed by a processor, the program instructions cause the processor to perform the foregoing method for measuring building operational energy consumption carbon emissions with high resolution.
[62] The module or unit integrated in the terminal device, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such an understanding, all or some of processes for implementing the method in the foregoing embodiments of the present disclosure can be completed by a computer program instructing relevant hardware. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, steps of the foregoing method embodiments may be implemented. The computer program includes computer program code, and the computer program code may be source code, object code, an executable file, some intermediate forms, or the like. The computer-readable medium may include: any physical entity or apparatus capable of carrying the computer program code, a recording medium, a USB disk, a mobile hard disk drive, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), a software distribution medium, and the like.
[63] Each example of the present specification is described in a progressive manner, each example focuses on the difference from other examples, and the same and similar parts between the examples may refer to each other. Since the system disclosed in the embodiments corresponds to the method disclosed in the embodiments, the description is relatively simple, and reference can be made to the method description.
[64] In this specification, several specific embodiments are used for illustration of the principles and implementations of the present disclosure. The description of the foregoing embodiments is used to help illustrate the method of the present disclosure and the core ideas thereof. In addition, those of ordinary skill in the art can make various modifications in terms of specific implementations and the scope of application in accordance with the ideas of the present disclosure. In conclusion, the content of this specification shall not be construed as a limitation to the present disclosure.
Claims (5)
1. A method for measuring building operational energy consumption carbon emissions with high resolution, comprising: calculating building operational energy consumption carbon emissions of each provincial-level administrative region; collecting auxiliary variable information of each provincial-level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and creating respective grid maps of the auxiliary variable information, wherein the auxiliary variable information comprises a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days; conducting zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, wherein the statistics comprise a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions comprise the provincial-level administrative regions and sub provincial-level administrative regions; constructing, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable; and inputting, based on scale invariance of a relational model, grid map statistics of the sub provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modifying the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub provincial-level administrative regions.
2. The method for measuring building operational energy consumption carbon emissions with high resolution according to claim 1, wherein after the inputting, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modifying the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, the method further comprises: performing a spatial distribution simulation based on the building operational energy consumption carbon emissions of the provincial-level administrative regions and the building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, to obtain a spatial distribution simulation result of building operational energy consumption carbon emissions corresponding to each grid map of the administrative regions; wherein the calculating building operational energy consumption carbon emissions of each provincial-level administrative region specifically comprises: obtaining energy consumption data of each provincial-level administrative region within a building climate zone, wherein the energy consumption data comprises primary energy consumption, heat consumption, and electricity consumption; and calculating the building operational energy consumption carbon emissions of each provincial level administrative region according to the energy consumption data, wherein the building operational energy consumption carbon emissions areC02 emissions in an energy consumption process of buildings in an operational stage; wherein a calculation formula for the building operational energy consumption carbon emissions of the provincial-level administrative region is as follows: BECCEi, = ECip x ai, + ECihx aih + ECie x ae; wherein BECCEip is building operational energy consumption carbon emissions of province p in year i, ECip is primary energy consumption of province p in year i, ECih is heat consumption of province p in year i, ECe is electricity consumption of province p in year i, air is a carbon emission factor corresponding to the primary energy consumption of province p in year i, aih is acarbon emission factor corresponding to the heat consumption of province p in year i, and aie a carbon emission factor corresponding to the electricity consumption of province p in year i; wherein the sub-provincial-level administrative region comprises one or more of a prefecture level administrative region, a county-level administrative region, and a township-level administrative region.
3. A system for measuring building operational energy consumption carbon emissions with high resolution, comprising: a carbon emissions calculation module, configured to calculate building operational energy consumption carbon emissions of each provincial-level administrative region; a grid map creating module, configured to collect auxiliary variable information of each provincial-level administrative region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and create respective grid maps of the auxiliary variable information, wherein the auxiliary variable information comprises a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days; a zonal statistics module, configured to conduct zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, wherein the statistics comprise a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions comprise the provincial-level administrative regions and sub-provincial-level administrative regions; a provincial downscaling model construction module, configured to construct, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the building operational energy consumption carbon emissions of the provincial-level administrative regions as a dependent variable; and a carbon emissions stepwise simulation module, configured to input, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modify the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions; further comprising: a spatial simulation module, configured to perform a spatial distribution simulation based on the building operational energy consumption carbon emissions of the provincial-level administrative regions and the building operational energy consumption carbon emissions of the sub-provincial-level administrative regions, to obtain a spatial distribution simulation result of building operational energy consumption carbon emissions corresponding to each grid map of the administrative regions; wherein the carbon emissions calculation module specifically comprises: an energy consumption data obtaining unit, configured to obtain energy consumption data of each provincial-level administrative region within a building climate zone, wherein the energy consumption data comprises primary energy consumption, heat consumption, and electricity consumption; and a carbon emissions calculation unit, configured to calculate the building operational energy consumption carbon emissions of each provincial-level administrative region according to the energy consumption data, wherein the building operational energy consumption carbon emissions are C02 emissions in an energy consumption process of buildings in an operational stage.
4. A terminal device, comprising a processor and a memory connected to the processor, wherein the memory is configured to store a computer program that comprises program instructions, and the processor is configured to invoke the program instructions to perform the method for measuring building operational energy consumption carbon emissions with high resolution according to any one of claims I to 2.
5. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program that comprises program instructions, and when executed by a processor, the program instructions cause the processor to perform the method for measuring building operational energy consumption carbon emissions with high resolution according to any one of claims 1 to 2.
－1/3－ 28 Jun 2021
DRAWINGS 101
Calculate building operational energy consumption carbon emissions of each provincial-level administrative region 102
Collect auxiliary variable information of each provincial-level administrative 2021103663
region based on building characteristics, social and economic conditions, regional climate background, and ambient microclimate of buildings, and create respective grid maps of the auxiliary variable information, where the auxiliary variable information includes a building area, a total population, a gross regional product, a temperature, heating degree days, and cooling degree days
103
Conduct zonal statistics on all the grid maps by using boundaries of administrative regions as boundary ranges, to obtain statistics of each of the grid maps, where the statistics include a gross regional product per unit area, a total population per unit area, an average temperature, average heating degree days, and average cooling degree days within each administrative boundary, and the administrative regions include the provincial-level administrative regions and sub-provincial-level administrative regions 104
Construct, based on a partial least squares regression method, a provincial downscaling model by using the statistics as independent variables and the operational energy consumption and carbon emissions of buildings of the provincial-level administrative regions as a dependent variable 105
Input, based on scale invariance of a relational model, grid map statistics of the sub-provincial-level administrative regions into the provincial downscaling model by using a stepwise downscaling method, and modify the inputted statistics by using a consistency-in-aggregation approach, to obtain building operational energy consumption carbon emissions of the sub-provincial-level administrative regions
FIG. 1
－2/3－
FIG. 2
－3/3－ 28 Jun 2021 2021103663
Building energy consumption carbon emissions density in Xiamen Unit: tons of CO2/km2
FIG. 3
201 Carbon emissions calculation module
202 Grid map creating module
203 Zonal statistics module
204 Provincial downscaling model construction module
205 Carbon emissions stepwise simulation module
FIG. 4
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